About the job
At Judgment Labs, we specialize in developing cutting-edge infrastructure for Agent Behavior Monitoring (ABM). Unlike conventional observability tools that merely track exceptions and latency, our ABM technology identifies behavioral anomalies, such as instruction drifts and context retrieval losses, in large-scale production settings.
Our solutions are trusted by numerous teams working on autonomous agents to gain insights into system behavior post-deployment. Rather than simply reacting to incidents, our clients analyze patterns across conversations and workflows, correlate regressions with specific interaction types, and identify critical points of reliability failure. Recently, we secured over $30 million across two funding rounds from notable investors like Lightspeed, SV Angel, and Valor Equity Partners.
The Role:
We are seeking a Senior Data Infrastructure Engineer to architect and enhance the real-time data pipelines essential for robust agent behavior analysis at scale. This position plays a vital role in processing hundreds of thousands of traces per second, executing LLM-based scoring and clustering in near-real-time, and ensuring low-latency query performance, which allows teams to monitor agent behavior as it unfolds. Ideal candidates will have experience designing petabyte-scale data systems, optimizing OLAP database performance, and managing the full data lifecycle from ingestion to analytics.
What You'll Do:
Design and automate large-scale, high-performance streaming and batch data processing systems to support Judgment's behavioral analysis products.
Collaborate closely with infrastructure and backend teams to enhance scalability, data governance, and operational efficiency.
Promote best practices in software engineering for data infrastructure at scale.
Uphold high standards for data quality and engineering: ensuring reliability, efficiency, documentation, testability, and maintainability.
Craft data models for optimal storage and access, ensuring efficient data flows to meet critical product requirements.
Enhance OLAP database performance through careful schema design, partitioning strategies, storage optimization, and access pattern analysis.

